Musical key classification

Classifying keys in a piece of music using a Hidden Markov Model trained using Spotify data.

Let's visualize the Chroma vectors.

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

df = pd.read_csv('../example/39C5FuZ8C8M0QI8CrMsPkR.txt')
df

	start	duration	confidence	pitch	chroma
0	0.00000	0.11279	0.000	1.000	C (also B♯, D#)
1	0.00000	0.11279	0.000	0.707	C♯, D♭ (also B#)
2	0.00000	0.11279	0.000	0.374	D (also C#, E#)
3	0.00000	0.11279	0.000	0.391	D♯, E♭ (also F#)
4	0.00000	0.11279	0.000	0.390	E (also D#, F♭)
...	...	...	...	...	...
18199	466.14078	1.49923	0.382	0.672	G (also F#, A#)
18200	466.14078	1.49923	0.382	0.563	G♯, A♭
18201	466.14078	1.49923	0.382	0.490	A (also G#, B#)
18202	466.14078	1.49923	0.382	0.561	A♯, B♭ (also C#)
18203	466.14078	1.49923	0.382	0.445	B (also A#, C♭)

18204 rows × 5 columns

max_pitch = df.groupby('start')['pitch'].transform(max) == df['pitch']
linedf = df[max_pitch]
linedf

	start	duration	confidence	pitch	chroma
0	0.00000	0.11279	0.000	1.0	C (also B♯, D#)
13	0.11279	0.22390	1.000	1.0	C♯, D♭ (also B#)
25	0.33669	0.13324	0.459	1.0	C♯, D♭ (also B#)
39	0.46993	0.20295	0.484	1.0	D♯, E♭ (also F#)
55	0.67288	0.24562	0.729	1.0	G (also F#, A#)
...	...	...	...	...	...
18154	464.90414	0.19773	0.176	1.0	A♯, B♭ (also C#)
18156	465.10187	0.53959	0.479	1.0	C (also B♯, D#)
18173	465.64145	0.23755	0.373	1.0	F (also E♯, G#)
18185	465.87900	0.26177	0.114	1.0	F (also E♯, G#)
18194	466.14078	1.49923	0.382	1.0	D (also C#, E#)

1520 rows × 5 columns

fig, ax = plt.subplots(figsize=(10, 5))
sns.scatterplot(x='start', y='chroma', size='pitch',
    data=df[df['start'] < 5], ax=ax, hue='chroma')
sns.lineplot(x='start', y='chroma', alpha=0.5,
    data=linedf[linedf['start'] < 5], ax=ax, sort=False)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.xlabel('time (in seconds)')
plt.title('Chroma vectors of first 5 seconds of \'Foreplay / Long Time\' track.')

Text(0.5, 1.0, "Chroma vectors of first 5 seconds of 'Foreplay / Long Time' track.")

from matplotlib.animation import FuncAnimation
def create_frame(frame, ax): # arguments are `frame number` and `fargs`
    ax.cla()
    window = 5 # show a window of 5 seconds
    step = frame/20 # advance 0.05 seconds per frame
    x1 = -window/2 + step
    x2 = window/2 + step
    scattersamples = (df['start'] >= x1) & (df['start'] <= x2)
    sns.scatterplot(x='start', y='chroma', size='pitch',
        data=df[scattersamples], ax=ax, hue='chroma', legend=False)
    linesamples = (linedf['start'] >= x1) & (linedf['start'] <= x2)
    sns.lineplot(x='start', y='chroma', alpha=0.5,
        data=linedf[linesamples], ax=ax, sort=False, legend=False)
    ax.set_xlim(x1, x2)
    line1, = ax.plot(
            [x1+window/2, x1+window/2],
            ['C (also B♯, D#)', 'B (also A#, C♭)'],
            '--', linewidth=2, label='Dashes set retroactively')
    plt.xlabel('time (in seconds)')
    plt.title('Chroma vectors of first 5 seconds of \'Foreplay / Long Time\' track.')
fig = plt.figure()
ax = fig.gca()
create_frame(0, ax)

from IPython.display import HTML
from celluloid import Camera
fig = plt.figure()
ax = fig.gca()
animation = FuncAnimation(fig, create_frame, frames=300, fargs=(ax,),
    interval=50) # Interval at 1000/50 = 20 frames per second
HTML(animation.to_jshtml())

Once Loop Reflect

animation.save('../example/39C5FuZ8C8M0QI8CrMsPkR.gif', writer='imagemagick', fps=20)

import IPython
IPython.display.Audio("../example/39C5FuZ8C8M0QI8CrMsPkR.mp3")